An automatic approach to identify word sense changes in text media across timescales. (16th April 2015)

• Record Type: Journal Article
• Authors: MITRA, SUNNY; MITRA, RITWIK; MAITY, SUMAN KALYAN; RIEDL, MARTIN; BIEMANN, CHRIS; GOYAL, PAWAN; MUKHERJEE, ANIMESH
• Editors: Kozareva, Zornitsa; Nastase, Vivi; Mihalcea, Rada
• Is Part Of: Natural language engineering. Volume 21:Part 5(2015)
• Page Start: 773
• Page End: 798
• Publication Date: 2015-04-16
• Subjects: Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S135132491500011X
• Languages: English
• ISSNs: 1351-3249

Abstract

Abstract: In this paper, we propose an unsupervised and automated method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books and millions of tweets posted per day. We construct distributional-thesauri-based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we propose a split/join based approach to compare the sense clusters at two different time points to find if there is 'birth' of a new sense. The approach also helps us to find if an older sense was 'split' into more than one sense or a newer sense has been formed from the 'join' of older senses or a particular sense has undergone 'death'. We use this completely unsupervised approach (a) within the Google books data to identify word sense differences within a media, and (b) across Google books and Twitter data to identify differences in word sense distribution across different media. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet.